python遗传算法实例
时间: 2023-10-13 17:27:55 浏览: 68
以下是一个简单的Python遗传算法实例,该算法用于解决一个二进制字符串的最大化问题。该问题的目标是找到一个长度为10的二进制字符串,使其包含尽可能多的1。
首先,我们需要定义一个适应度函数,用于评估每个个体的适应度。在这个例子中,适应度函数将返回二进制字符串中包含的1的数量。
```python
import random
def fitness_function(chromosome):
# calculate fitness value of a chromosome
return sum(chromosome)
```
接下来,我们需要定义一个种群,并生成一些随机的个体。
```python
population_size = 10
population = []
# generate initial population
for i in range(population_size):
chromosome = [random.randint(0, 1) for _ in range(10)]
population.append(chromosome)
```
然后,我们需要定义遗传算法的主要步骤。在每代中,我们将计算每个个体的适应度,并根据适应度进行选择和交叉。
```python
generations = 100
for generation in range(generations):
# evaluate fitness of each chromosome in population
fitness_values = [fitness_function(chromosome) for chromosome in population]
# select parents for crossover
parents = []
for _ in range(int(population_size/2)):
parent1 = population[fitness_values.index(max(fitness_values))]
fitness_values[fitness_values.index(max(fitness_values))] = -1
parent2 = population[fitness_values.index(max(fitness_values))]
fitness_values[fitness_values.index(max(fitness_values))] = -1
parents.append((parent1, parent2))
# crossover parents to create new children
children = []
for parent1, parent2 in parents:
crossover_point = random.randint(1, 8)
child1 = parent1[:crossover_point] + parent2[crossover_point:]
child2 = parent2[:crossover_point] + parent1[crossover_point:]
children.append(child1)
children.append(child2)
# mutate children with small probability
for i in range(len(children)):
for j in range(len(children[i])):
if random.random() < 0.1:
children[i][j] = 1 - children[i][j]
# replace old population with new population of children
population = children
```
最后,我们可以输出最优解。
```python
# find chromosome with highest fitness value in final population
fitness_values = [fitness_function(chromosome) for chromosome in population]
best_chromosome = population[fitness_values.index(max(fitness_values))]
# print best solution
print("Best solution:", best_chromosome)
```
这个例子只是一个简单的遗传算法实现,但可以作为一个入门级别的参考。在实际应用中,遗传算法还可以与其他优化算法结合使用,以解决更复杂的问题。
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